






'''simple docstring'''





import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()
def __UpperCAmelCase   (					A					:			List[Any]			,       A					:			Any			,       A					:			Optional[int]			,       A					:			List[str]		)    ->   Tuple:
			# Initialise PyTorch model
			UpperCAmelCase_      :       List[str]          =       FunnelConfig.from_json_file(A		)
			print(F"Building PyTorch model from configuration: {config}"		)
			UpperCAmelCase_      :       int          =       FunnelBaseModel(A		) if base_model else FunnelModel(A		)

			# Load weights from tf checkpoint
			load_tf_weights_in_funnel(A			,       A			,       A		)

			# Save pytorch-model
			print(F"Save PyTorch model to {pytorch_dump_path}"		)
			torch.save(model.state_dict()			,       A		)


if __name__ == "__main__":
						_UpperCamelCase						:						List[Any]  =					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
						)
						parser.add_argument(
						    '--config_file',
						    default=None,
						    type=str,
						    required=True,
						    help='The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.',
						)
						parser.add_argument(
						    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)
						parser.add_argument(
						    '--base_model', action='store_true', help='Whether you want just the base model (no decoder) or not.'
						)
						_UpperCamelCase						:						Tuple  =					parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
						)










'''simple docstring'''





def __UpperCAmelCase   (					A					:			list		)    ->   list:
			if len(A		) <= 1:
						return lst

			UpperCAmelCase_      :       List[str]          =       1

			while i < len(A		):
						if lst[i - 1] <= lst[i]:
									i += 1
						else:
									UpperCAmelCase_    ,      UpperCAmelCase_      :       Dict          =       lst[i], lst[i - 1]
									i -= 1
									if i == 0:
												UpperCAmelCase_      :       Dict          =       1

			return lst


if __name__ == "__main__":
						_UpperCamelCase						:						int  =					input('Enter numbers separated by a comma:\n').strip()
						_UpperCamelCase						:						Union[str, Any]  =					[int(item) for item in user_input.split(',')]
						print(gnome_sort(unsorted))


